where \(\mathbf {W} \in \mathbb {R}^{2F \times P}\)  is the stacked matrix of \(\mathrm {W}_i\) , \(\mathrm {S}^{r} \in \mathbb {R}^{3 \times P}\)  is the rigid shape. So that in this case \(\mathrm {rank}(\mathbf {W}) \le 3\) , and Tomasi & Kanade [1]} use a truncated Singular Value Decomposition (SVD) of \(\mathbf {W}\)  to recover the cameras and the rigid shape. This property is used in Sec. REF  to derive a measure of rigidity.
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